Anxiety is a state of apprehensive anticipation and is associated with heightened tension, arousal, and negative valence [1]. Automatic detection of anxiety has received attention in several fields including human-computer interaction [2], intelligent transport systems [3], security and access control [4], workload assessment [5], and health monitoring [6]. More recently, automatic detection of anxiety has been suggested as a means to provide an objective measure that can complement clinical anxiety treatment programs [7]. In fact, management of physiological symptoms is an integral part of existing anxiety treatment programs. These programs, however, typically rely on the individual's ability to self-reflect and self-recognize physiological signs of anxiety and are not well-suited to the needs of clinical populations who have intellectual disability and/or deficits in communication, introspection, and emotional self-awareness (e.g., those with autism spectrum disorder (ASD)). In these populations, automatic anxiety detection can serve to reduce some of these barriers to potency and applicability of anxiety treatments by providing individuals with an objective measure of their internal state. However, automatic anxiety detection methods have not been examined in the context of therapeutic settings and clinical populations to date.
Conceptually, anxiety presents across three interconnected dimensions: behavioral (e.g., crying, avoidance, tantrums), subjective-cognitive (e.g., maladaptive and negative thoughts), and physiological [8]. The latter can be non-invasively measured using inexpensive and wearable sensors and has therefore received attention in studies examining automatic detection of anxiety. The physiological response to anxiogenic stimuli has been documented in neurotypical individuals: the stimulus is perceived and interpreted as a threat by structures in the central nervous system, and peripheral and endocrine systems are aroused to mobilize the body's resources for responding to the situation. This arousal response is often accompanied by a series of physiological changes that can be measured non-invasively. These changes include increased cardiovascular activity (e.g., increased heart rate), as well as changes in perspiration (e.g., measured through electrodermal activity) and skin temperature [9]. A technical challenge in detection of physiological arousal is the inter- and intra-personal variations in physiological measures. For example, across individuals, anatomical and neurochemical differences result in variability in basal heart rate and in cardiac reactivity to anxiogenic stimuli. In the same individual, physiological processes (e.g., sinus rhythm and respiratory sinus arrhythmia) and external factors such as diet, sleep, or mental and emotional states also lead to additional heart rate variability.
To model this variability, several studies have used supervised methods that rely on training data and have shown that arousal associated with anxiety can be detected physiologically with high accuracy. For example, time and frequency domain features from 5-minute segments of electrocardiography (ECG), electromyography, respiration, and skin conductivity measurements were used in [3] to classify three stress levels (low, medium, and high) using discriminant analysis. Secondary analysis also showed that heart rate and skin conductance measurements provided the highest correlation with stress levels. In the context of continuous stress monitoring, one-minute segments of ECG, skin conductance, and accelerometer signals were used in [6] to differentiate periods of physical activity from mental stress using three classification schemes (decision trees, Bayesian network, and support network machines). A fuzzy logic system was proposed by de Santos Sierra et al. [4] to classify stress and baseline conditions using skin conductance and heart rate measurements. Zhai and Barreto [2] proposed a support vector machine classifier for detecting stress based on cardiac activity, skin conductance, skin temperature, and pupil diameter.
However, it would be useful to provide a system for detecting anxiety that is not reliant on training data.